Snorkel AI, a company focused on making AI practical, announced its launch out of stealth with $15M total funding from Greylock, GV, In-Q-Tel and others, and its end-to-end Machine Learning (ML) platform, Snorkel Flow. Snorkel Flow enables developers and non-developers alike to build and deploy AI applications in a fraction of the time by programmatically labeling and managing the “training data” that fuels modern AI. Paying customers using Snorkel Flow include top U.S. banks, government agencies, and other large enterprises.
Manually managing, building, and labeling large training datasets has emerged as one of the most significant bottlenecks to the adoption of AI, with the process often requiring weeks or months of manual effort for each application. While at the Stanford AI Lab, the Snorkel AI founding team – Alex Ratner (Assistant Professor at the University of Washington), Chris Ré (Associate Professor at Stanford and 2015 MacArthur Genius Fellow), Paroma Varma, Braden Hancock, and Henry Ehrenberg – saw how this training data issue was becoming the key problem in AI. After spending four years developing and deploying technology to solve this problem with companies like Google, Intel and Apple, and organizations like DARPA and Stanford Hospital, the team spun out to launch Snorkel AI and build an end-to-end platform that made this technology accessible to all enterprises.
“Despite spending billions of dollars on AI, few organizations have been able to use it as widely and effectively as they want to. This is because available solutions either ignore the most important part of AI today – the labeled training data that fuels modern approaches – or rely on armies of human labelers to produce it,” said Alex Ratner, CEO of Snorkel AI. “Our end-to-end platform, Snorkel Flow, focuses on a new programmatic approach to the training data that enables enterprises to use AI where they couldn’t before.”
Snorkel Flow is a first-of-its-kind ML platform that uses a novel programmatic approach to building and labeling the “training datasets” that fuel modern AI. Users can drive the end-to-end development process without spending months manually labeling and managing data. Instead, users develop “labeling functions,” or rules or heuristics, and other programmatic operators, which Snorkel Flow automatically integrates to train state-of-the-art machine learning models. Users can easily improve and adapt these models just by editing their programmatic training data in Snorkel Flow’s guided interface. Snorkel Flow is especially impactful for the many sectors where data is extremely difficult to label and manage by hand, as it requires expensive subject matter experts and must be kept on-premises due to privacy constraints.
Snorkel AI customers have already saved months of time, and are applying AI to new problems that they couldn’t tackle before. For example, a top-three U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their loan portfolio, including for a recent time-sensitive use case that the bank had estimated would have taken months of manual labeling efforts. With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours, and that could be quickly and easily adapted to new problems and business lines.
“Snorkel Flow is the first end-to-end ML platform that focuses on the data, making AI a reality for enterprises,” said Saam Motamedi, Partner at Greylock and Snorkel Board Member. “We’ve consistently heard from Fortune 500 CIOs that they have been disappointed with their progress using AI, largely because they get stuck on the data. Customers’ rapid success with Snorkel Flow is a testament to the power of this new, data-centric approach, which has the potential to democratize AI across the enterprise. We are thrilled to partner with the Snorkel team as they drive this important market shift.”
“The time, expertise, and costs involved in labeling training data present significant challenges to the U.S. government in applying AI to missions of national security,” said A.J. Bertone, a Partner at In-Q-Tel. “Snorkel AI provides a revolutionary capability that can greatly reduce the level of effort required to develop mission-ready machine learning models by addressing this critical data problem.”
About Snorkel AI
Founded by a team spun out of the Stanford AI Lab, Snorkel AI’s mission is to make AI practical by focusing on the training data problem. Snorkel Flow is the first end-to-end machine learning platform that programmatically labels and manages the training data. Backed by Greylock, GV, and In-Q-Tel, the team is based in Palo Alto. For more information on Snorkel AI, please visit: https://www.snorkel.ai/